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import numpy as np
import librosa
import torch
from .init import pipe
LIMIT = 90 # limit 90 seconds
TASK = "transcribe"
class A2T:
def __init__(self, mic):
self.mic = mic
def __preprocces(self, audio, frame_rate):
try:
print("Audio before : ", audio)
audio = audio / 32678.0
print("Audio div : ", audio)
if len(audio.shape) > 1:
audio = librosa.to_mono(audio.T)
print("Audio mono : ", audio)
if frame_rate != 16_000:
audio = librosa.resample(audio, orig_sr=frame_rate, target_sr=16000)
print("Audio resample : ", audio)
audio = audio[:16_000*LIMIT]
print("Audio cut : ", audio)
audio = torch.tensor(audio)
print("Audio torch : ", audio)
return audio
except Exception as e:
print("Preprocces error", e)
return None
def __transcribe(self, inputs, task: str = None):
if inputs is None:
print("Inputs None")
transcribed_text = pipe(inputs, generate_kwargs={"task": task}, return_timestamps=True)["text"]
return transcribed_text
def predict(self):
if self.mic is not None:
audio = self.mic
# frame_rate = self.mic.frame_rate
else:
return "please provide audio"
try:
# forced_decoder_ids = processor.get_decoder_prompt_ids(language="english", task="transcribe")
# # audio = self.__preprocces(audio=audio, frame_rate=frame_rate)
# inputs = processor(audio=audio, sampling_rate=16000, return_tensors="pt")
# predicted_ids = model.generate(**inputs, max_length=400, forced_decoder_ids=forced_decoder_ids)
# transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
return self.__transcribe(inputs=audio, task=TASK)
except Exception as e:
print("Predict error", e)
return "Oops some kinda error"
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